This paper describes algorithms for the animation of men and women performing three dynamic athletic behaviors: running, bicycling, and vaulting. We animate these behaviors using control algorithms that cause a physically realistic model to perform the desired maneuver. For example, control algorithms allow the simulated humans to maintain balance while moving their arms, to run or bicycle at a variety of speeds, and to perform a handspring vault. Algorithms for group behaviors allow a number of simulated bicyclists to ride as a group while avoiding simple patterns of obstacles. We add secondarymotion to the animations with springmass simulations of clothing driven by the rigid-body motion of the simulated human. For each simulation, we compare the computed motion to that of humans performing similar maneuvers both qualitatively through the comparison of real and simulated video images and quantitatively through the comparison of simulated and biomechanical data.

Image rendering maps scene parameters to output pixel values; animation maps motion-control parameters to trajectory values. Because these mapping functions are usually multidimensional, nonlinear, and discontinuous, #nding input parameters that yield desirable output values is often a painful process of manual tweaking. Interactiveevolution and inverse design are two general methodologies for computer-assisted parameter setting in which the computer plays a prominent role. In this paper we present another such methodology.

This paper describes an algorithm for automatically adapting existing simulated behaviors to new characters. Animating a new character is difficult because a control system tuned for one character will not, in general, work on a character with different limb lengths, masses, or moments of inertia. The algorithm presented here adapts the control system to a new character in two stages. First, the control system parameters are scaled based on the sizes, masses, and moments of inertia of the new and the original characters. Then a subset of the parameters is fine-tuned using a search process based on simulated annealing. To demonstrate the effectiveness of this approach, we animate the running motion of a woman, child, and imaginary character by modifying the control system for a man. We also animate the bicycling motion of a second imaginary character by modifying the control system for a man. We evaluate the results of this approach by comparing the motion of the simulated human runners...

Animation through the numerical simulation of physics-based graphics models offers unsurpassed realism, but it can be computationally demanding. Likewise, finding controllers that enable physics-based models to produce desired animations usually entails formidable computational cost. This paper demonstrates the possibility of replacing the numerical simulation and control of model dynamics with a dramatically more efficient alternative. In particular, we propose the NeuroAnimator, a novel approach to creating physically realistic animation that exploits neural networks. NeuroAnimators are automatically trained off-line to emulate physical dynamics through the observation of physics-based models in action. Depending on the model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conventional numerical simulation. Furthermore, by exploiting the network structure of the NeuroAnimator, we introduce a fast algorithm for learning controllers that enables either physics-based models or their neural network emulators to synthesize motions satisfying prescribed animation goals. We demonstrate NeuroAnimators for passive and active (actuated) rigid body, articulated, and deformable physics-based models.

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This paper presents a novel physics-based representation of realistic character motion. The dynamical model incorporates several factors of locomotion derived from the biomechanical literature, including relative preferences for using some muscles more than others, elastic mechanisms at joints due to the mechanical properties of tendons, ligaments, and muscles, and variable stiffness at joints depending on the task. When used in a spacetime optimization framework, the parameters of this model define a wide range of styles of natural human movement.

The ability to learn is a potentially compelling and important quality for interactive synthetic characters. To that end, we describe a practical approach to real-time learning for synthetic characters. Our implementation is grounded in the techniques of reinforcement learning and informed by insights from animal training. It simpli- es the learning task for characters by (a) enabling them to take advantage of predictable regularities in their world, (b) allowing them to make maximal use of any supervisory signals, and (c) making them easy to train by humans.

Seemingly simple behaviors such as human walking are difficult to model because of their inherent instability. Kinematic animation techniques can freely ignore such intrinsically dynamic problems, but they therefore also miss modeling important motion characteristics. On the other hand, the effect of balancing can emerge in a physically-based animation, but it requires computing delicate control strategies. We propose an alternative method that adds closedloop feedback to open-loop periodic motions. We then apply our technique to create robust walking gaits for a fully-dynamic 19 degree -of-freedom human model. Important global characteristics such as direction, speed and stride rate can be controlled by changing the open-loop behavior alone or through simple control parameters, while continuing to employ the same local stabilization technique. Among other features, our dynamic "human" walking character is thus able to follow desired paths specified by the animator. Keywords: control...

One of the main limitations of scalability in body-brain evolution systems is the representation chosen for encoding creatures. This paper defines a class of representations called generative representations, which are identified by their ability to reuse elements of the genotype in the translation to the phenotype. This paper presents an example of a generative representation for the concurrent evolution of the morphology and neural controller of simulated robots, and also introduces GENRE, an evolutionary system for evolving designs using this representation. Applying GENRE to the task of evolving robots for locomotion and comparing it against a non-generative (direct) representation shows that the generative representation system rapidly produces robots with significantly greater fitness. Analyzing these results shows

We propose the use of interactive, user-in-the-loop techniques for controlling physically-based animated characters. With a suitably designed interface, the continuous and discrete input actions afforded by a standard mouse and keyboard allow for the creation of a broad range of motions. We apply our techniques to interactively control planar dynamic simulations of a bounding cat, a gymnastic desk lamp, and a human character capable of walking, running, climbing, and various gymnastic behaviors. The interactive control techniques allows a performer's intuition and knowledge about motion planning to be readily exploited. Video games are the current target application of this work. CR Categories: I.3.6 [ Computer Graphics]: Methodology and Techniques--- Interaction Techniques; I.3.7 [ Computer Graphics ]: Three-Dimensional Graphics and Realism---Animation; I.6.8 [ Simulation and Modeling]: Types of Simulation---Animation Keywords: physically based animation, user interfaces 1 Introduc...